Diffusion magnetic resonance imaging (dMRI) is a medical imaging modality used to model the anatomical network of neuronal fibers in the white matter (WM) of the brain, in vivo. By measuring spatial degrees of water diffusion in the WM with diffusion weighted images (DWI), one can estimate the most probable directions of fiber bundles in each voxel of the brain. Then, with these probabilistic peak directions at each voxel, tractography algorithms may be invoked to trace out the most anatomically accurate fiber tracts in the brain, the results of which are used to study neurological diseases through computational anatomy, network analysis and diagnostic classification.
To reconstruct an accurate white matter fiber network, probability density functions, modeled as either an ensemble average propagator (EAP) or orientation distribution function (ODF) at each voxel, must be accurately estimated from the dMRI signals and then the function peaks must be identified. Peak finding algorithms suffer from noise and discretization, and are subject to an accurate probability distribution to begin with. Furthermore, to acquire an accurate dMRI signal and represent locally complex (e.g., crossing) fibers, many samples in the wavevector (q−) space must be measured. High Angular Resolution Diffusion Imaging (HARDI) was developed to sample with high angular resolution on single or multiple shells in q-space to overcome the limitations of a single peak assumption of Diffusion Tensor Imaging (DTI). However, reconstructing accurate signals using methods such as HARDI or Diffusion Spectrum Imaging (DSI), which samples q-space densely on the Cartesian grid, require longer scan times which impedes greatly on their clinical applicability.
Accordingly, it is desired to reduce the scan time of dMRI protocols such as HARDI or DSI without compromising the quality of the signal reconstruction, while accurately finding probability peaks for tractography without losing accuracy through first estimating EAPs or ODF s.